1. Field
This disclosure relates to understanding and retrieving music.
2. Description of the Related Art
Currently, the field of music retrieval has followed the methods used for text retrieval including semantic tagging and organization techniques. Characters became samples, words became frames, documents became songs. Currently, music may be expressed as a feature vector of signal-derived statistics, which may approximate the ear, as in machine listening approaches. Alternately, music may be expressed by the collective reaction to the music in terms of sales data, shared collections, or lists of favorite songs. The signal-derived approaches may predict, with some accuracy, the genre or style of a piece of music, or compute acoustic similarity, or detect what instruments are being used in which key, or discern the high-level structure of music to tease apart verse from chorus.
It is believed that current systems for retrieving music ignore the “meaning” of music, where “meaning” may be defined as what happens in between the music and the reaction. It is believed that current systems do not have the capability to learn how songs make people feel, and current systems do not understand why some artists are currently selling millions of records, and other artists are not. It is believed that current retrieval systems are stuck inside a perceptual box—only being able to feel the vibrations without truly understanding the effect of music or its cause.